| { |
| "nbformat": 4, |
| "nbformat_minor": 0, |
| "metadata": { |
| "colab": { |
| "provenance": [], |
| "collapsed_sections": [ |
| "FH3IRpYTta2v" |
| ] |
| }, |
| "kernelspec": { |
| "display_name": "Python 3", |
| "name": "python3" |
| } |
| }, |
| "cells": [ |
| { |
| "cell_type": "markdown", |
| "metadata": { |
| "id": "FH3IRpYTta2v" |
| }, |
| "source": [ |
| "##### Copyright 2023 The IREE Authors" |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "metadata": { |
| "id": "mWGa71_Ct2ug", |
| "cellView": "form" |
| }, |
| "source": [ |
| "#@title Licensed under the Apache License v2.0 with LLVM Exceptions.\n", |
| "# See https://llvm.org/LICENSE.txt for license information.\n", |
| "# SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception" |
| ], |
| "execution_count": 1, |
| "outputs": [] |
| }, |
| { |
| "cell_type": "markdown", |
| "metadata": { |
| "id": "h5s6ncerSpc5" |
| }, |
| "source": [ |
| "# Dynamic Shapes\n", |
| "\n", |
| "This notebook\n", |
| "\n", |
| "1. Creates a PyTorch program with dynamic shapes using [iree-turbine](https://github.com/iree-org/iree-turbine)'s advanced AOT toolkit\n", |
| "2. Compiles the program to an IREE VM bytecode module\n", |
| "3. Tests running the compiled VM module using IREE's runtime\n", |
| "4. Downloads compilation artifacts for use with the native (C API) sample application" |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "metadata": { |
| "id": "s2bScbYkP6VZ", |
| "colab": { |
| "base_uri": "https://localhost:8080/" |
| }, |
| "outputId": "fede6b57-a87b-42fc-ab71-57c1b8ff4ab3" |
| }, |
| "source": [ |
| "#@title General setup\n", |
| "\n", |
| "import os\n", |
| "import tempfile\n", |
| "\n", |
| "ARTIFACTS_DIR = os.path.join(tempfile.gettempdir(), \"iree\", \"colab_artifacts\")\n", |
| "os.makedirs(ARTIFACTS_DIR, exist_ok=True)\n", |
| "print(f\"Using artifacts directory '{ARTIFACTS_DIR}'\")" |
| ], |
| "execution_count": 2, |
| "outputs": [ |
| { |
| "output_type": "stream", |
| "name": "stdout", |
| "text": [ |
| "Using artifacts directory '/tmp/iree/colab_artifacts'\n" |
| ] |
| } |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "source": [ |
| "%%capture\n", |
| "#@title Uninstall existing packages\n", |
| "# This avoids some warnings when installing specific PyTorch packages below.\n", |
| "!python -m pip uninstall -y fastai torchaudio torchdata torchtext torchvision" |
| ], |
| "metadata": { |
| "id": "y9KOsqosg6Ms" |
| }, |
| "execution_count": 3, |
| "outputs": [] |
| }, |
| { |
| "cell_type": "code", |
| "source": [ |
| "#@title Install iree-turbine\n", |
| "\n", |
| "# Limit cell height.\n", |
| "from IPython.display import Javascript\n", |
| "display(Javascript('''google.colab.output.setIframeHeight(0, true, {maxHeight: 300})'''))\n", |
| "\n", |
| "!python -m pip install iree-turbine" |
| ], |
| "metadata": { |
| "id": "SdCAvI3sqBO7", |
| "outputId": "2d38c722-33cf-4210-89a7-bf4f42f92ab9", |
| "colab": { |
| "base_uri": "https://localhost:8080/", |
| "height": 300 |
| } |
| }, |
| "execution_count": 4, |
| "outputs": [ |
| { |
| "output_type": "display_data", |
| "data": { |
| "text/plain": [ |
| "<IPython.core.display.Javascript object>" |
| ], |
| "application/javascript": [ |
| "google.colab.output.setIframeHeight(0, true, {maxHeight: 300})" |
| ] |
| }, |
| "metadata": {} |
| }, |
| { |
| "output_type": "stream", |
| "name": "stdout", |
| "text": [ |
| "Collecting iree-turbine\n", |
| " Downloading iree_turbine-3.1.0-py3-none-any.whl.metadata (6.7 kB)\n", |
| "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from iree-turbine) (1.26.4)\n", |
| "Collecting iree-base-compiler (from iree-turbine)\n", |
| " Downloading iree_base_compiler-3.1.0-cp310-cp310-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl.metadata (1.0 kB)\n", |
| "Collecting iree-base-runtime (from iree-turbine)\n", |
| " Downloading iree_base_runtime-3.1.0-cp310-cp310-manylinux_2_28_x86_64.whl.metadata (1.0 kB)\n", |
| "Requirement already satisfied: Jinja2>=3.1.3 in /usr/local/lib/python3.10/dist-packages (from iree-turbine) (3.1.5)\n", |
| "Collecting ml_dtypes>=0.5.0 (from iree-turbine)\n", |
| " Downloading ml_dtypes-0.5.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (21 kB)\n", |
| "Requirement already satisfied: typing_extensions in /usr/local/lib/python3.10/dist-packages (from iree-turbine) (4.12.2)\n", |
| "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from Jinja2>=3.1.3->iree-turbine) (3.0.2)\n", |
| "Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from iree-base-compiler->iree-turbine) (1.13.1)\n", |
| "Requirement already satisfied: mpmath<1.4,>=1.1.0 in /usr/local/lib/python3.10/dist-packages (from sympy->iree-base-compiler->iree-turbine) (1.3.0)\n", |
| "Downloading iree_turbine-3.1.0-py3-none-any.whl (301 kB)\n", |
| "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m301.7/301.7 kB\u001b[0m \u001b[31m5.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", |
| "\u001b[?25hDownloading ml_dtypes-0.5.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (4.7 MB)\n", |
| "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m4.7/4.7 MB\u001b[0m \u001b[31m21.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", |
| "\u001b[?25hDownloading iree_base_compiler-3.1.0-cp310-cp310-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl (71.2 MB)\n", |
| "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m71.2/71.2 MB\u001b[0m \u001b[31m7.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", |
| "\u001b[?25hDownloading iree_base_runtime-3.1.0-cp310-cp310-manylinux_2_28_x86_64.whl (8.2 MB)\n", |
| "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m8.2/8.2 MB\u001b[0m \u001b[31m25.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", |
| "\u001b[?25hInstalling collected packages: ml_dtypes, iree-base-runtime, iree-base-compiler, iree-turbine\n", |
| " Attempting uninstall: ml_dtypes\n", |
| " Found existing installation: ml-dtypes 0.4.1\n", |
| " Uninstalling ml-dtypes-0.4.1:\n", |
| " Successfully uninstalled ml-dtypes-0.4.1\n", |
| "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n", |
| "tensorflow 2.17.1 requires ml-dtypes<0.5.0,>=0.3.1, but you have ml-dtypes 0.5.1 which is incompatible.\u001b[0m\u001b[31m\n", |
| "\u001b[0mSuccessfully installed iree-base-compiler-3.1.0 iree-base-runtime-3.1.0 iree-turbine-3.1.0 ml_dtypes-0.5.1\n" |
| ] |
| } |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "source": [ |
| "#@title Report version information\n", |
| "!echo \"Installed iree-turbine, $(python -m pip show iree_turbine | grep Version)\"\n", |
| "\n", |
| "!echo -e \"\\nInstalled IREE, compiler version information:\"\n", |
| "!iree-compile --version\n", |
| "\n", |
| "import torch\n", |
| "print(\"\\nInstalled PyTorch, version:\", torch.__version__)" |
| ], |
| "metadata": { |
| "colab": { |
| "base_uri": "https://localhost:8080/" |
| }, |
| "id": "Oj5I6R9LI7t_", |
| "outputId": "deaa1abf-dc0e-49d8-d165-47d53592d94f" |
| }, |
| "execution_count": 5, |
| "outputs": [ |
| { |
| "output_type": "stream", |
| "name": "stdout", |
| "text": [ |
| "Installed iree-turbine, Version: 3.1.0\n", |
| "\n", |
| "Installed IREE, compiler version information:\n", |
| "IREE (https://iree.dev):\n", |
| " IREE compiler version 3.1.0rc20250107 @ d2242207764230ad398585a5771f9d54ce91b4c8\n", |
| " LLVM version 20.0.0git\n", |
| " Optimized build\n", |
| "\n", |
| "Installed PyTorch, version: 2.5.1+cu121\n" |
| ] |
| } |
| ] |
| }, |
| { |
| "cell_type": "markdown", |
| "source": [ |
| "## Create a program using PyTorch + iree-turbine\n", |
| "\n", |
| "NOTE: as in other domains, providing more information to a compiler allows it\n", |
| "to generate more efficient code. As a general rule, the slowest varying\n", |
| "dimensions of program data like batch index or timestep are safer to treat as\n", |
| "dynamic than faster varying dimensions like image x/y/channel. See\n", |
| "[this paper](https://arxiv.org/pdf/2006.03031.pdf) for a discussion of the\n", |
| "challenges imposed by dynamic shapes and one project's approach to addressing\n", |
| "them." |
| ], |
| "metadata": { |
| "id": "C3mhaullI940" |
| } |
| }, |
| { |
| "cell_type": "code", |
| "source": [ |
| "#@title Define a sample `torch.nn.Module`.\n", |
| "\n", |
| "import iree.turbine.aot as aot\n", |
| "\n", |
| "class DynamicShapesModule(torch.nn.Module):\n", |
| " # reduce_sum_1d (dynamic input size, static output size)\n", |
| " # tensor<?xi32> -> tensor<i32>\n", |
| " # e.g. [1, 2, 3] -> 6\n", |
| " def reduce_sum_1d(self, values):\n", |
| " return torch.sum(values)\n", |
| "\n", |
| " # reduce_sum_2d (partially dynamic input size, static output size)\n", |
| " # tensor<?x3xi32> -> tensor<3xi32>\n", |
| " # e.g. [[1, 2, 3], [10, 20, 30]] -> [11, 22, 33]\n", |
| " def reduce_sum_2d(self, values):\n", |
| " return torch.sum(values, 0)\n", |
| "\n", |
| " # add_one (dynamic input size, dynamic output size)\n", |
| " # tensor<?xi32>) -> tensor<?xi32>\n", |
| " # e.g. [1, 2, 3] -> [2, 3, 4]\n", |
| " def add_one(self, values):\n", |
| " return values + 1" |
| ], |
| "metadata": { |
| "id": "vsf9F4WxI_DX" |
| }, |
| "execution_count": 6, |
| "outputs": [] |
| }, |
| { |
| "cell_type": "code", |
| "source": [ |
| "#@title Export using FxProgramsBuilder.\n", |
| "\n", |
| "fxb = aot.FxProgramsBuilder(DynamicShapesModule())\n", |
| "\n", |
| "# Create a single dynamic export dimension.\n", |
| "dynamic_x = torch.export.Dim(\"x\")\n", |
| "# Example inputs with a mix of placeholder (dynamic) and static dimensions.\n", |
| "example_1d = torch.empty(16, dtype=torch.int32)\n", |
| "example_2d = torch.empty((16, 3), dtype=torch.int32)\n", |
| "\n", |
| "# Export reduce_sum_1d with a dynamic dimension.\n", |
| "@fxb.export_program(\n", |
| " args=(example_1d,),\n", |
| " dynamic_shapes={\"values\": {0: dynamic_x}},\n", |
| ")\n", |
| "def reduce_sum_1d(module, values):\n", |
| " return module.reduce_sum_1d(values)\n", |
| "\n", |
| "# Export reduce_sum_2d with one dynamic dimension.\n", |
| "@fxb.export_program(\n", |
| " args=(example_2d,),\n", |
| " dynamic_shapes={\"values\": {0: dynamic_x}},\n", |
| ")\n", |
| "def reduce_sum_2d(module, values):\n", |
| " return module.reduce_sum_2d(values)\n", |
| "\n", |
| "# Export add_one with a dynamic dimension.\n", |
| "@fxb.export_program(\n", |
| " args=(example_1d,),\n", |
| " dynamic_shapes={\"values\": {0: dynamic_x}},\n", |
| ")\n", |
| "def add_one(module, values):\n", |
| " return module.add_one(values)\n", |
| "\n", |
| "export_output = aot.export(fxb)" |
| ], |
| "metadata": { |
| "id": "cCy3nuLBKTAg" |
| }, |
| "execution_count": 7, |
| "outputs": [] |
| }, |
| { |
| "cell_type": "code", |
| "source": [ |
| "from iree.compiler.ir import Context\n", |
| "\n", |
| "imported_mlir_path = os.path.join(ARTIFACTS_DIR, \"dynamic_shapes.mlir\")\n", |
| "export_output.save_mlir(imported_mlir_path)\n", |
| "print(f\"Wrote MLIR to path '{imported_mlir_path}'\")\n", |
| "\n", |
| "# Inspect the IR.\n", |
| "# Note the question marks for dynamic shapes in types, like `tensor<?xi32>`.\n", |
| "print(\"\\nDynamic Shapes MLIR:\")\n", |
| "!cat {imported_mlir_path}" |
| ], |
| "metadata": { |
| "colab": { |
| "base_uri": "https://localhost:8080/" |
| }, |
| "id": "_OQIpOtNr4Gh", |
| "outputId": "abe96b74-88de-4979-959c-cdfbc981b17c" |
| }, |
| "execution_count": 8, |
| "outputs": [ |
| { |
| "output_type": "stream", |
| "name": "stdout", |
| "text": [ |
| "Wrote MLIR to path '/tmp/iree/colab_artifacts/dynamic_shapes.mlir'\n", |
| "\n", |
| "Dynamic Shapes MLIR:\n", |
| "module @module {\n", |
| " func.func @reduce_sum_1d(%arg0: !torch.vtensor<[?],si32>) -> !torch.vtensor<[],si64> attributes {torch.assume_strict_symbolic_shapes} {\n", |
| " %none = torch.constant.none\n", |
| " %0 = torch.aten.sum %arg0, %none : !torch.vtensor<[?],si32>, !torch.none -> !torch.vtensor<[],si64>\n", |
| " return %0 : !torch.vtensor<[],si64>\n", |
| " }\n", |
| " func.func @reduce_sum_2d(%arg0: !torch.vtensor<[?,3],si32>) -> !torch.vtensor<[3],si64> attributes {torch.assume_strict_symbolic_shapes} {\n", |
| " %int0 = torch.constant.int 0\n", |
| " %0 = torch.prim.ListConstruct %int0 : (!torch.int) -> !torch.list<int>\n", |
| " %false = torch.constant.bool false\n", |
| " %none = torch.constant.none\n", |
| " %1 = torch.aten.sum.dim_IntList %arg0, %0, %false, %none : !torch.vtensor<[?,3],si32>, !torch.list<int>, !torch.bool, !torch.none -> !torch.vtensor<[3],si64>\n", |
| " return %1 : !torch.vtensor<[3],si64>\n", |
| " }\n", |
| " func.func @add_one(%arg0: !torch.vtensor<[?],si32>) -> !torch.vtensor<[?],si32> attributes {torch.assume_strict_symbolic_shapes} {\n", |
| " %int1 = torch.constant.int 1\n", |
| " %int1_0 = torch.constant.int 1\n", |
| " %0 = torch.aten.add.Scalar %arg0, %int1, %int1_0 : !torch.vtensor<[?],si32>, !torch.int, !torch.int -> !torch.vtensor<[?],si32>\n", |
| " return %0 : !torch.vtensor<[?],si32>\n", |
| " }\n", |
| "}\n" |
| ] |
| } |
| ] |
| }, |
| { |
| "cell_type": "markdown", |
| "source": [ |
| "## Test the imported program\n", |
| "\n", |
| "_Note: you can stop after each step and use intermediate outputs with other tools outside of Colab._\n", |
| "\n", |
| "_See the [README](https://github.com/iree-org/iree/tree/main/samples/dynamic_shapes#instructions) for more details and example command line instructions._\n", |
| "\n", |
| "* _The \"imported MLIR\" (above) can be used by IREE's generic compiler tools_\n", |
| "* _The \"binary\" can be saved and used by runtime applications_\n", |
| "\n", |
| "_The specific point at which you switch from Python to native tools will depend on your project._" |
| ], |
| "metadata": { |
| "id": "z6w_Pbl6tUtJ" |
| } |
| }, |
| { |
| "cell_type": "code", |
| "source": [ |
| "# Compile to a file on disk for usage outside of Python.\n", |
| "flatbuffer_path = os.path.join(ARTIFACTS_DIR, \"dynamic_shapes_cpu.vmfb\")\n", |
| "export_output.compile(save_to=flatbuffer_path)\n", |
| "print(f\"Wrote compiled program to path '{flatbuffer_path}'\")\n", |
| "\n", |
| "# Compile into memory for testing.\n", |
| "binary = export_output.compile(save_to=None)" |
| ], |
| "metadata": { |
| "colab": { |
| "base_uri": "https://localhost:8080/" |
| }, |
| "id": "0PGyH1tvI_Ic", |
| "outputId": "2ac3f280-1834-4d6c-f5b0-c9b470549ca7" |
| }, |
| "execution_count": 9, |
| "outputs": [ |
| { |
| "output_type": "stream", |
| "name": "stdout", |
| "text": [ |
| "Wrote compiled program to path '/tmp/iree/colab_artifacts/dynamic_shapes_cpu.vmfb'\n" |
| ] |
| } |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "source": [ |
| "import iree.runtime as ireert\n", |
| "import numpy as np\n", |
| "\n", |
| "# Use the IREE runtime API to test the compiled program.\n", |
| "config = ireert.Config(\"local-task\")\n", |
| "vm_module = ireert.load_vm_module(\n", |
| " ireert.VmModule.wrap_buffer(config.vm_instance, binary.map_memory()),\n", |
| " config,\n", |
| ")\n", |
| "\n", |
| "print(vm_module.reduce_sum_1d(np.array([1, 10, 100], dtype=np.int32)).to_host())\n", |
| "print(vm_module.reduce_sum_2d(np.array([[1, 2, 3], [10, 20, 30]], dtype=np.int32)).to_host())\n", |
| "print(vm_module.reduce_sum_2d(np.array([[1, 2, 3], [10, 20, 30], [100, 200, 300]], dtype=np.int32)).to_host())\n", |
| "print(vm_module.add_one(np.array([1, 10, 100], dtype=np.int32)).to_host())" |
| ], |
| "metadata": { |
| "colab": { |
| "base_uri": "https://localhost:8080/" |
| }, |
| "id": "9ilJY15BI_LD", |
| "outputId": "f20aec4f-353e-4793-f9f1-066006d4471b" |
| }, |
| "execution_count": 10, |
| "outputs": [ |
| { |
| "output_type": "stream", |
| "name": "stdout", |
| "text": [ |
| "111\n", |
| "[11 22 33]\n", |
| "[111 222 333]\n", |
| "[ 2 11 101]\n" |
| ] |
| } |
| ] |
| }, |
| { |
| "cell_type": "markdown", |
| "source": [ |
| "## Download compilation artifacts" |
| ], |
| "metadata": { |
| "id": "3mizlpY9uJEW" |
| } |
| }, |
| { |
| "cell_type": "code", |
| "source": [ |
| "ARTIFACTS_ZIP = \"/tmp/dynamic_shapes_colab_artifacts.zip\"\n", |
| "\n", |
| "print(f\"Zipping '{ARTIFACTS_DIR}' to '{ARTIFACTS_ZIP}' for download...\")\n", |
| "!cd {ARTIFACTS_DIR} && zip -r {ARTIFACTS_ZIP} .\n", |
| "\n", |
| "# Note: you can also download files using Colab's file explorer\n", |
| "try:\n", |
| " from google.colab import files\n", |
| " print(\"Downloading the artifacts zip file...\")\n", |
| " files.download(ARTIFACTS_ZIP)\n", |
| "except ImportError:\n", |
| " print(\"Missing google_colab Python package, can't download files\")" |
| ], |
| "metadata": { |
| "colab": { |
| "base_uri": "https://localhost:8080/", |
| "height": 86 |
| }, |
| "id": "dgaXpdiWuGtx", |
| "outputId": "94823b69-1095-4a97-9974-7d36fb3e2fb8" |
| }, |
| "execution_count": 11, |
| "outputs": [ |
| { |
| "output_type": "stream", |
| "name": "stdout", |
| "text": [ |
| "Zipping '/tmp/iree/colab_artifacts' to '/tmp/dynamic_shapes_colab_artifacts.zip' for download...\n", |
| " adding: dynamic_shapes_cpu.vmfb (deflated 66%)\n", |
| " adding: dynamic_shapes.mlir (deflated 72%)\n", |
| "Downloading the artifacts zip file...\n" |
| ] |
| }, |
| { |
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| "data": { |
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| "application/javascript": [ |
| "\n", |
| " async function download(id, filename, size) {\n", |
| " if (!google.colab.kernel.accessAllowed) {\n", |
| " return;\n", |
| " }\n", |
| " const div = document.createElement('div');\n", |
| " const label = document.createElement('label');\n", |
| " label.textContent = `Downloading \"${filename}\": `;\n", |
| " div.appendChild(label);\n", |
| " const progress = document.createElement('progress');\n", |
| " progress.max = size;\n", |
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| "\n", |
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| " let downloaded = 0;\n", |
| "\n", |
| " const channel = await google.colab.kernel.comms.open(id);\n", |
| " // Send a message to notify the kernel that we're ready.\n", |
| " channel.send({})\n", |
| "\n", |
| " for await (const message of channel.messages) {\n", |
| " // Send a message to notify the kernel that we're ready.\n", |
| " channel.send({})\n", |
| " if (message.buffers) {\n", |
| " for (const buffer of message.buffers) {\n", |
| " buffers.push(buffer);\n", |
| " downloaded += buffer.byteLength;\n", |
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| " a.href = window.URL.createObjectURL(blob);\n", |
| " a.download = filename;\n", |
| " div.appendChild(a);\n", |
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| "download(\"download_7377c999-5cd8-4987-95c4-921d56969f65\", \"dynamic_shapes_colab_artifacts.zip\", 5472)" |
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